Yuanlong Xie

Adaptive two-degree-of-freedom PI for speed control of permanent magnet synchronous motor based on fractional order GPC.

In this paper, an adaptive two-degree-of-freedom (2Dof) proportional-integral (PI) controller is proposed for the speed control of permanent magnet synchronous motor (PMSM). Firstly, an enhanced just-in-time learning technique consisting of two novel searching engines is presented to identify the model of the speed control system in a real-time manner. Secondly, a general formula is given to predict the future speed reference which is unavailable at the interval of two bus-communication cycles. Thirdly, the fractional order generalized predictive control (FOGPC) is introduced to improve the control performance of the servo drive system. Based on the identified model parameters and predicted speed reference, the optimal control law of FOGPC is derived. Finally, the designed 2Dof PI controller is auto-tuned by matching with the optimal control law. Simulations and real-time experimental results on the servo drive system of PMSM are provided to illustrate the effectiveness of the proposed strategy.

Fractional order modeling and high-frequency repetitive motion control for flexible swing arm system

Flexible swing arm system (FSAS) with flexibility and nonlinearity is the key component of die bonder in LED packaging industry. This paper explores the application of fractional calculus on modeling and high-frequency repetitive motion control for FSAS. Considering of the complex characteristics of high-frequency repetitive motion on different stages, fractional order models are designed to describe FSAS more accurately. The proposed fractional order models are identified using particle swarm optimization (PSO) algorithm. Based on the comprehensive control performance evaluation, proportional integral (PI) controllers are optimized for FSAS according to the identified fractional order models. Simulation and experiments will be conducted to demonstrate the existence and practical viability of the proposed fractional order models for the FSAS.

Model-free tuning strategy of fractional-order PI controller for speed regulation of permanent magnet synchronous motor:

This paper investigates a model-free tuning method of a fractional-order proportional–integral (FOPI) controller and its application for the speed regulation of a permanent magnet synchronous motor (PMSM). Firstly, the presented practical FOPI tuning method formulates the FOPI controller parameter identification problem via virtual reference feedback tuning (VRFT). Under the lack of accurate models, the proposed model-free method depends only on the measured input–output data of the closed-loop PMSM servo system. Secondly, Bode’s ideal transfer function is incorporated into the VRFT with consideration of the systematic fractional dynamics. Thus, the properties of the resulting system may be approximated to the desired fractional-order reference model. Thirdly, the proposed method fully considers optimal performance constraints on the stability requirements, sensitivity criteria, frequency-domain and time-domain characteristics. Then, the comprehensive optimization problem is derived and solved. Using suitably tuned parameters, the robustness and disturbance rejection ability of the VRFT-based FOPI control system are enhanced to achieve optimal performance. The convergence of the proposed method is proved by theoretical analysis. Finally, experimental results are presented to illustrate the effectiveness of the proposed model-free FOPI control method for the PMSM servo system.

Data-driven adaptive fractional order PI control for PMSM servo system with measurement noise and data dropouts

In this paper, data-driven adaptive fractional order proportional integral (AFOPI) control is presented for permanent magnet synchronous motor (PMSM) servo system perturbed by measurement noise and data dropouts. The proposed method directly exploits the closed-loop process data for the AFOPI controller design under unknown noise distribution and data missing probability. Firstly, the proposed method constructs the AFOPI controller tuning problem as a parameter identification problem using the modified lp norm virtual reference feedback tuning (VRFT). Then, iteratively reweighted least squares is integrated into the lp norm VRFT to give a consistent compensation solution for the AFOPI controller. The measurement noise and data dropouts are estimated and eliminated by feedback compensation periodically, so that the AFOPI controller is updated online to accommodate the time-varying operating conditions. Moreover, the convergence and stability are guaranteed by mathematical analysis. Finally, the effectiveness of the proposed method is demonstrated both on simulations and experiments implemented on a practical PMSM servo system.

Iterative-learning integral-plus-proportional control of a flexible swing arm system for position trajectory tracking

In the LED packaging industry, the position trajectory tracking of a flexible swing arm system is influenced by the unknown disturbances and parametric uncertainties. In this paper, an iterative-learning integral-plus-proportional (IP) controller is proposed to enhance the efficiency and accuracy of the flexible swing arm system. First, online prediction mechanism is incorporated with the iterative-learning automatic tuning to obtain an optimum of the controller parameters, referred to as predictive iterative-learning control. Then, the adaptive iterative-learning IP controller is derived using the proposed predictive iterative-learning control method. To achieve faster convergence, virtual reference feedback tuning is used to obtain the initial parameters of IP controller. In this method, only input/output measured data of the controlled plant are fully utilized by means of dynamic linearization technique. Moreover, the proposed tuning method can optimize the controller online using experimental data during normal system operation. The convergence and stability properties of the closed-loop system are analyzed. Finally, simulations and experiments on real-time flexible swing arm system show the effectiveness of the predictive iterative-learning IP control method.

Chattering-free hybrid control for speed regulation of permanent magnet synchronous motor

In this paper, a novel chattering-free hybrid control (HC) algorithm is proposed for the speed regulation of permanent magnet synchronous motor (PMSM). The HC is made up of the high-order sliding mode control (HOSMC) scheme and the proportional-integral (PI) control scheme, where HOSMC is developed for transient tracking, and PI is responsible for steady regulation. The error band method is adopted to determine the switching rule between the HOSMC and PI schemes. When the operation condition with strong disturbances happens and the tracking error exceeds the error band, the HOSMC scheme is chosen to be the main controller due to its characteristics of fast response and strong robustness. Under the control of HOSMC, the tracking error will converge to zero gradually. However, the undesirable chattering is generated by the discontinuous and high-frequency switching control action in HOSMC scheme. On the other hand, when the amplitude of the disturbances decreases and the tracking error enters the error band, the PI scheme will replace HOSMC to be the control core such that the actual speed will track the speed reference without chattering or steady error. The main advantages of the proposed HC algorithm are that the chattering is eliminated by the PI scheme, and the strong robustness is guaranteed by the HOSMC scheme. Real-time experiments in embedded platform are conducted to verify the efficiency and superiority of the HC algorithm.